Why it matters
Profile
Shared memory layer for AI agents with open engram format (YAML). Useful for persistent learning patterns in Hermes workflows.
setup mediumintegration highinterface cli
Provenance
Signals
Listed in the awesome-hermes-agent README
Sources: 2 / Surfaces: 1
Fast skim
What the upstream surface says
Short excerpt only, so you can decide whether to click out.
Persistent memory for AI agents. Local-first, zero-cost, works across MCP tools.
plur.ai · Benchmark · Engram Spec · npm
You correct your agent's coding style on Monday. On Tuesday, it makes the same mistake. You explain your architecture in Cursor. That night, Claude Code has no idea.
PLUR — Your agents share the same memoryThe ideaInstallTell your agentManual setup (Claude Code)Global install (faster startup)OpenClawHermes Agent
- Activation — retrieval strength that decays over time (ACT-R model) and strengthens on access. Stale facts naturally fade from injection without manual cleanup.
- Feedback signals — positive/negative ratings that train injection quality over time
- Scope — hierarchical namespace (global, project:myapp, cluster:prod, service:api) controlling where the engram applies
- Polarity — automatic classification of "do" vs "don't" rules, so constraints are injected separately from directives
- Associations — links to other engrams, including co-access edges that form automatically when engrams are recalled together
- Node.js 18+
- 2GB RAM minimum — the embedding model (ONNX runtime) needs ~1GB for installation. On servers with less RAM, embeddings are skipped and search falls back to BM25 keyword matching.
- Bug reports — issue with reproduction steps